PCA-Filtered Clustering on KD6 Essential Data
Mervin M Fansler
07 November 2022
Purpose
We perform clustering of features and perturbations to identify isoforms that exhibit similar perturbation outcomes.
Initialization
Libraries
library(magrittr)
library(tidyverse)
library(pheatmap)
library(bluster)Parameters
set.seed(20210818)
## filtering options
## number of cells in perturbation
MIN_CELLS=30
## max NTPs with NA WUI
MAX_NTP_NA=0.20
## min avg TPM in NTPs
MIN_MEAN_TPM=20
## input files
FILE_SE_WUI="tbl/df_wui_kd6_essential_ui10.Rds"
## output files
FILE_OUT_TARGETS_RAW="tbl/df_target_raw_nnclusters_kd6_essential_ui10.csv"
FILE_OUT_TARGETS="tbl/df_target_nnclusters_kd6_essential_ui10.csv"
FILE_OUT_GENES="tbl/df_gene_nnclusters_kd6_essential_ui10.csv"
FILE_OUT_FINAL="img/heatmap-kd6-essential-nnclusters-labeled.pdf"
RDS_DWUI=sprintf("data/dwui/%s-dwui-target-gene-kd6-essential.Rds", format(Sys.time(), '%Y%m%d'))
RDS_ZDWUI=sprintf("data/dwui/%s-zdwui-target-gene-kd6-essential.Rds", format(Sys.time(), '%Y%m%d'))
## aesthetics
NCOLORS=100
COLORS_BWR <- colorRampPalette(c("blue", "white", "red"))(NCOLORS)
COLORS_MKY <- colorRampPalette(c("magenta", "black", "yellow"))(NCOLORS)
COLORS_YKM <- colorRampPalette(c("yellow", "black", "magenta"))(NCOLORS)
COLORS_RKG <- colorRampPalette(c("red", "black", "green"))(NCOLORS)
breaks_dwui <- seq(-0.5, 0.5, length.out=NCOLORS + 1)
breaks_zdwui <- seq(-4, 4, length.out=NCOLORS + 1)
breaks_zdwui_broad <- seq(-6, 6, length.out=NCOLORS + 1)
breaks_pca <- seq(-20, 20, length.out=NCOLORS + 1)Data
Loading
df_wui <- readRDS(FILE_SE_WUI)Preprocessing
sgid2gene <- df_wui %>%
dplyr::select(sgID_AB, target_gene) %>%
distinct(sgID_AB, target_gene) %>%
deframe()
ens2gene <- df_wui %>%
dplyr::select(gene_id, gene_name) %>%
distinct(gene_id, gene_name) %>%
deframe()
convert_rownames <- function (mat, in2out) {
mat %>% set_rownames(in2out[rownames(.)])
}
convert_colnames <- function (mat, in2out) {
mat %>% set_colnames(in2out[colnames(.)])
}
df_ntp <- filter(df_wui, target_gene == 'non-targeting') %>%
group_by(gene_id, gene_name) %>%
filter(mean(is.na(wui)) <= MAX_NTP_NA) %>%
filter(mean(tpm) >= MIN_MEAN_TPM) %>%
filter(!is.na(wui)) %>%
summarize(mean_wui=weighted.mean(wui, n_cells), .groups='drop',
sd_wui=sqrt(sum((wui-mean_wui)^2)/n()))
df_dwui <- df_wui %>%
filter(target_gene != 'non-targeting',
n_cells >= MIN_CELLS) %>%
inner_join(df_ntp, by=c("gene_id", "gene_name")) %>%
mutate(dwui=wui-mean_wui) %>%
dplyr::select(gene_id, sgID_AB, wui, dwui)
wui_gene_target <- df_dwui %>%
dplyr::select(gene_id, sgID_AB, wui) %>%
pivot_wider(id_cols="gene_id", names_from="sgID_AB", values_from="wui") %>%
column_to_rownames("gene_id") %>%
as.matrix
dwui_gene_target <- df_dwui %>%
dplyr::select(gene_id, sgID_AB, dwui) %>%
pivot_wider(id_cols="gene_id", names_from="sgID_AB", values_from="dwui") %>%
column_to_rownames("gene_id") %>%
as.matrix
zdwui_gene_target <- t(dwui_gene_target) %>% scale(center=FALSE) %>% t()
zdwui_gene_target_clean <- zdwui_gene_target
zdwui_gene_target_clean[is.na(zdwui_gene_target_clean)] <- 0PCA
Compute PCA
res_pca <- BiocSingular::runPCA(t(zdwui_gene_target_clean), rank=30, center=FALSE)
mat_pca <- res_pca$xHeatmap - All Components
pheatmap(mat_pca,
color=COLORS_YKM,
breaks=breaks_pca,
fontsize_col=1, fontsize_row=1,
show_colnames=FALSE, show_rownames=FALSE, scale='none',
cluster_rows=TRUE, cluster_cols=TRUE)
### Compute Clusters
clusters_targets_raw <- clusterRows(mat_pca, NNGraphParam(k=5, cluster.fun="walktrap"))
names(clusters_targets_raw) <- rownames(mat_pca)
idx_clusters_targets <- order(clusters_targets_raw)
df_row_annots <- data.frame(cluster_id_target=clusters_targets_raw,
row.names=names(clusters_targets_raw))
table(clusters_targets_raw)## clusters_targets_raw
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 114 70 15 7 30 23 27 462 15 13 44 96 28 51 779 27 17 25 22 8
## 21 22 23 24 25 26 27 28
## 28 48 52 10 23 32 3 8Export Target Clusters
df_wui %>%
distinct(target_gene, target_gene_id, sgID_AB) %>%
inner_join(tibble(sgID_AB=rownames(mat_pca), cluster_id=clusters_targets_raw), by="sgID_AB") %>%
write_csv(FILE_OUT_TARGETS_RAW)Plot Clusters
pheatmap(mat_pca[idx_clusters_targets,],
color=COLORS_YKM,
breaks=breaks_pca,
fontsize_col=1, fontsize_row=1,
annotation_row=df_row_annots,
show_colnames=FALSE, show_rownames=FALSE,
cluster_rows=FALSE, cluster_cols=TRUE, scale='none',
cutree_rows=NA)
It appears clusters 8 and 15 represent sets of perturbations with null responses.
Z-DWUI Plot
Potential Null Response Clusters
We want to check the potential null-response clusters in the DWUI representation.
Cluster 8
idx_cluster <- clusters_targets_raw %>% `[`(. == 8) %>% names()
pheatmap(t(zdwui_gene_target[,idx_cluster]),
color=COLORS_YKM,
breaks=breaks_zdwui,
fontsize_col=1, fontsize_row=1,
show_colnames=FALSE, show_rownames=FALSE, scale='none',
cluster_rows=TRUE, cluster_cols=TRUE)
Cluster 15
idx_cluster <- clusters_targets_raw %>% `[`(. == 15) %>% names()
pheatmap(t(zdwui_gene_target[,idx_cluster]),
color=COLORS_YKM,
breaks=breaks_zdwui,
fontsize_col=1, fontsize_row=1,
show_colnames=FALSE, show_rownames=FALSE, scale='none',
cluster_rows=TRUE, cluster_cols=TRUE)
This looks mostly like noise in both clusters.
All Other Clusters
idx_cluster <- clusters_targets_raw %>% `[`(!(. %in% c(8,15))) %>% names
pheatmap(t(zdwui_gene_target[,idx_cluster]),
color=COLORS_YKM,
breaks=breaks_zdwui,
fontsize_col=1, fontsize_row=1,
annotation_row=df_row_annots,
show_colnames=FALSE, show_rownames=FALSE, scale='none',
cluster_rows=TRUE, cluster_cols=TRUE)
This looks good. Now we need to eliminate the noise columns (readout genes).
Establish Regulating Perturbations
idx_signal_targets <- clusters_targets_raw %>% `[`(!(. %in% c(8,15))) %>% namesWe identify 836 genes as coherent response genes.
Cluster Response Genes
Reduced PCA
res_pca2 <- BiocSingular::runPCA(zdwui_gene_target_clean[,idx_signal_targets], rank=30, center=FALSE)
mat_pca2 <- res_pca2$xpheatmap(t(mat_pca2),
color=COLORS_YKM,
breaks=breaks_pca,
fontsize_col=1, fontsize_row=1,
show_colnames=FALSE, show_rownames=FALSE, scale='none',
cluster_rows=TRUE, cluster_cols=TRUE)
Compute Gene Clusters
clusters_genes <- clusterRows(mat_pca2, NNGraphParam(k=4, cluster.fun="walktrap"))
names(clusters_genes) <- rownames(mat_pca2)
idx_clusters_genes <- order(clusters_genes)
df_col_annots <- data.frame(cluster_id_gene=clusters_genes,
row.names=names(clusters_genes))
table(clusters_genes)## clusters_genes
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 282 101 120 53 19 23 28 36 272 26 44 181 439 82 19 7 16 14 9 4Export Gene Clusters
df_wui %>%
distinct(gene_name, gene_id) %>%
inner_join(enframe(clusters_genes, name="gene_id", value="cluster_id"), by="gene_id") %>%
write_csv(FILE_OUT_GENES)Plot Clusters
pheatmap(t(mat_pca2[idx_clusters_genes,]),
color=COLORS_YKM,
breaks=breaks_pca,
fontsize_col=1, fontsize_row=1,
annotation_col=df_col_annots,
show_colnames=FALSE, show_rownames=FALSE,
cluster_rows=TRUE, cluster_cols=FALSE, scale='none')
Cluster 9
idx_cluster <- clusters_genes %>% `[`(. == 9) %>% names()
pheatmap(t(zdwui_gene_target[idx_cluster, idx_signal_targets]),
color=COLORS_YKM,
breaks=breaks_zdwui,
annotation_row=df_row_annots,
fontsize_col=1, fontsize_row=1,
show_colnames=FALSE, show_rownames=FALSE, scale='none',
cluster_rows=TRUE, cluster_cols=TRUE)
Cluster 12
idx_cluster <- clusters_genes %>% `[`(. == 12) %>% names()
pheatmap(t(zdwui_gene_target[idx_cluster, idx_signal_targets]),
color=COLORS_YKM,
breaks=breaks_zdwui,
annotation_row=df_row_annots,
fontsize_col=1, fontsize_row=1,
show_colnames=FALSE, show_rownames=FALSE, scale='none',
cluster_rows=TRUE, cluster_cols=TRUE)
Cluster 13
idx_cluster <- clusters_genes %>% `[`(. == 13) %>% names()
pheatmap(t(zdwui_gene_target[idx_cluster, idx_signal_targets]),
color=COLORS_YKM,
breaks=breaks_zdwui,
annotation_row=df_row_annots,
fontsize_col=1, fontsize_row=1,
show_colnames=FALSE, show_rownames=FALSE, scale='none',
cluster_rows=TRUE, cluster_cols=TRUE)
Clusters 9, 12, and 13 appear to be mostly non-responsive.
Establish Signal Genes
idx_signal_genes <- clusters_genes %>% `[`(!(. %in% c(9,12,13))) %>% namesWe identify 883 genes as coherent response genes.
pheatmap(t(zdwui_gene_target[idx_signal_genes, idx_signal_targets]),
color=COLORS_BWR,
breaks=breaks_zdwui,
fontsize_col=1, fontsize_row=1,
annotation_row=df_row_annots,
annotation_col=df_col_annots,
show_colnames=FALSE, show_rownames=FALSE, scale='none',
cluster_rows=TRUE, cluster_cols=TRUE, clustering_method="complete")
Recompute Target Clusters
zdwui_target_gene_final <- t(zdwui_gene_target_clean[idx_signal_genes, idx_signal_targets])
res_pca3 <- BiocSingular::runPCA(zdwui_target_gene_final, rank=30, center=FALSE)
mat_pca3 <- res_pca3$x
clusters_targets_final <- clusterRows(mat_pca3, BLUSPARAM=NNGraphParam(k=3, cluster.fun="walktrap"))
names(clusters_targets_final) <- rownames(mat_pca3)
clusters_genes_final <- clusters_genes %>% `[`(!(. %in% c(9,12,13)))
idx_clusters_targets_final <- clusters_targets_final %>% sort %>% names
idx_clusters_genes_final <- clusters_genes_final %>% sort %>% names
df_row_annots_final <- data.frame(cluster_id_target=clusters_targets_final,
row.names=names(clusters_targets_final))
table(clusters_targets_final)## clusters_targets_final
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## 26 41 132 30 98 91 130 39 75 10 21 52 39 6 11 19 8 8Export Final Target Clusters
df_wui %>%
distinct(target_gene, target_gene_id, sgID_AB) %>%
inner_join(tibble(sgID_AB=rownames(mat_pca3), cluster_id=clusters_targets_final), by="sgID_AB") %>%
write_csv(FILE_OUT_TARGETS)Final Heatmap
pheatmap(zdwui_target_gene_final[idx_clusters_targets_final, idx_clusters_genes_final],
color=COLORS_BWR,
breaks=breaks_zdwui,
fontsize_col=1, fontsize_row=1,
annotation_row=df_row_annots_final,
annotation_col=df_col_annots,
show_colnames=FALSE, show_rownames=FALSE, scale='none',
cluster_rows=FALSE, cluster_cols=FALSE, clustering_method="complete",
gaps_row=cumsum(table(clusters_targets_final)),
gaps_col=cumsum(table(clusters_genes_final)) %>% `[`(!duplicated(.)),
cutree_rows=NA, cutree_cols=NA)
Final Joint Plot
pheatmap(zdwui_target_gene_final[idx_clusters_targets_final, idx_clusters_genes_final],
color=COLORS_BWR,
breaks=breaks_zdwui,
fontsize_col=1, fontsize_row=1,
annotation_row=df_row_annots_final,
annotation_col=df_col_annots,
show_colnames=TRUE, show_rownames=TRUE, scale='none',
labels_row=sgid2gene[idx_clusters_targets_final],
labels_col=ens2gene[idx_clusters_genes_final],
cluster_rows=FALSE, cluster_cols=FALSE, clustering_method="complete",
gaps_row=cumsum(table(clusters_targets_final)),
gaps_col=cumsum(table(clusters_genes_final)) %>% `[`(!duplicated(.)),
cutree_rows=NA, cutree_cols=NA,
filename=FILE_OUT_FINAL, width=16, height=16)saveRDS(t(dwui_gene_target), RDS_DWUI)
saveRDS(t(zdwui_gene_target), RDS_ZDWUI)Runtime Details
Session Info
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS/LAPACK: /Users/mfansler/miniconda3/envs/bioc_3_14/lib/libopenblasp-r0.3.18.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] bluster_1.4.0 pheatmap_1.0.12 forcats_0.5.1 stringr_1.4.0
## [5] dplyr_1.0.8 purrr_0.3.4 readr_2.1.1 tidyr_1.1.4
## [9] tibble_3.1.7 ggplot2_3.3.5 tidyverse_1.3.1 magrittr_2.0.3
##
## loaded via a namespace (and not attached):
## [1] matrixStats_0.61.0 fs_1.5.2 bit64_4.0.5
## [4] lubridate_1.8.0 RColorBrewer_1.1-2 httr_1.4.2
## [7] tools_4.1.1 backports_1.4.0 bslib_0.3.1
## [10] utf8_1.2.2 R6_2.5.1 irlba_2.3.5
## [13] DBI_1.1.1 BiocGenerics_0.40.0 colorspace_2.0-2
## [16] withr_2.4.3 tidyselect_1.1.1 bit_4.0.4
## [19] compiler_4.1.1 cli_3.3.0 rvest_1.0.2
## [22] BiocNeighbors_1.12.0 xml2_1.3.3 DelayedArray_0.20.0
## [25] sass_0.4.0 scales_1.1.1 digest_0.6.29
## [28] rmarkdown_2.11 pkgconfig_2.0.3 htmltools_0.5.2
## [31] MatrixGenerics_1.6.0 dbplyr_2.1.1 fastmap_1.1.0
## [34] highr_0.9 rlang_1.0.2 readxl_1.3.1
## [37] rstudioapi_0.13 farver_2.1.0 jquerylib_0.1.4
## [40] generics_0.1.1 jsonlite_1.7.2 vroom_1.5.7
## [43] BiocParallel_1.28.0 BiocSingular_1.10.0 Matrix_1.3-4
## [46] Rcpp_1.0.7 munsell_0.5.0 S4Vectors_0.32.0
## [49] fansi_0.5.0 lifecycle_1.0.1 stringi_1.7.6
## [52] yaml_2.2.1 grid_4.1.1 parallel_4.1.1
## [55] crayon_1.4.2 lattice_0.20-45 haven_2.4.3
## [58] beachmat_2.10.0 hms_1.1.1 knitr_1.39
## [61] pillar_1.7.0 igraph_1.2.9 ScaledMatrix_1.2.0
## [64] stats4_4.1.1 reprex_2.0.1 glue_1.6.2
## [67] evaluate_0.15 modelr_0.1.8 vctrs_0.4.1
## [70] tzdb_0.2.0 cellranger_1.1.0 gtable_0.3.0
## [73] assertthat_0.2.1 xfun_0.30 rsvd_1.0.5
## [76] broom_0.8.0 IRanges_2.28.0 cluster_2.1.2
## [79] ellipsis_0.3.2Conda Environment
## Conda Environment YAML
name: base
channels:
- conda-forge
- bioconda
- defaults
dependencies:
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prefix: /Users/mfansler/miniconda3